Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "48" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460009 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.305786 | 1.168818 | -0.325643 | 1.610320 | -1.016927 | 2.223608 | -0.601211 | -1.909023 | 0.5717 | 0.5930 | 0.3581 | nan | nan |
| 2460008 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.091841 | 1.291554 | -0.339463 | 1.907531 | -0.721738 | 1.869974 | -0.668772 | 0.474583 | 0.6229 | 0.6398 | 0.3237 | nan | nan |
| 2460007 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.012414 | 0.911010 | -0.545265 | 1.400825 | 1.348095 | 1.414839 | -0.625062 | -2.060348 | 0.5761 | 0.6002 | 0.3453 | nan | nan |
| 2459999 | not_connected | 0.00% | 89.97% | 85.96% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1491 | 0.1719 | 0.0856 | nan | nan |
| 2459998 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.133336 | 0.815620 | -0.350076 | 1.384748 | -1.040652 | 2.396548 | -0.672886 | -1.943562 | 0.5687 | 0.5946 | 0.3762 | nan | nan |
| 2459997 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.081753 | 0.755540 | -0.106474 | 1.507832 | -1.024270 | 2.071446 | -0.392997 | -3.108375 | 0.5980 | 0.6228 | 0.3848 | nan | nan |
| 2459996 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.073028 | 1.118047 | -0.337671 | 1.644562 | -1.178603 | 1.945032 | -0.750841 | -1.447168 | 0.5980 | 0.6192 | 0.3978 | nan | nan |
| 2459995 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.009896 | 0.894489 | -0.324555 | 1.666186 | -1.046480 | 2.277508 | -0.813605 | -1.789803 | 0.5986 | 0.6222 | 0.3834 | nan | nan |
| 2459994 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.124332 | 0.792943 | -0.273037 | 1.516176 | -1.108793 | 1.926993 | -0.750606 | -1.981585 | 0.5892 | 0.6146 | 0.3795 | nan | nan |
| 2459993 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.516058 | 1.443162 | -0.056784 | 1.760495 | -0.854307 | 2.293846 | -0.429399 | -1.734360 | 0.5816 | 0.6248 | 0.3930 | nan | nan |
| 2459991 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.575933 | 1.135149 | -0.189094 | 1.795240 | -0.751342 | 2.236178 | -0.301760 | -1.827805 | 0.5877 | 0.6086 | 0.3880 | nan | nan |
| 2459990 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.420048 | 1.110711 | -0.219910 | 1.915465 | -0.746005 | 2.445525 | 0.213195 | -1.713274 | 0.5870 | 0.6130 | 0.3882 | nan | nan |
| 2459989 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.198505 | 1.023737 | 0.006582 | 1.670375 | -1.001253 | 1.578307 | -0.479915 | -2.016665 | 0.5851 | 0.6126 | 0.3898 | nan | nan |
| 2459988 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.402914 | 1.434347 | -0.200704 | 1.964339 | -0.983071 | 2.890034 | -0.603398 | -1.737267 | 0.5727 | 0.5979 | 0.3732 | nan | nan |
| 2459987 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.134490 | 0.814139 | -0.230716 | 1.507241 | -0.801897 | 1.506969 | 0.505154 | -0.669629 | 0.5956 | 0.6181 | 0.3757 | nan | nan |
| 2459986 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.402236 | 1.382886 | -0.231229 | 1.871140 | -0.975592 | 2.579377 | -0.773249 | 0.764554 | 0.6022 | 0.6270 | 0.3415 | nan | nan |
| 2459985 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.057692 | 1.578542 | -0.350049 | 1.471238 | 1.070859 | 1.280298 | -0.497144 | -2.752312 | 0.5877 | 0.6084 | 0.3798 | nan | nan |
| 2459984 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.980854 | 7.045926 | 4.346316 | 1.340158 | 9.593825 | 2.800261 | 4.548056 | 1.711592 | 0.0402 | 0.5970 | 0.4510 | nan | nan |
| 2459983 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.002944 | 0.854672 | 0.545468 | 1.803028 | -0.304304 | 2.593406 | -0.294674 | -0.476995 | 0.6258 | 0.6489 | 0.3134 | nan | nan |
| 2459982 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.131301 | 0.111655 | -0.088910 | 1.036449 | -0.750819 | -0.158411 | -0.864113 | -0.349317 | 0.6574 | 0.6626 | 0.3028 | nan | nan |
| 2459981 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.033850 | 0.857183 | 0.640668 | 2.205754 | -0.309054 | 2.717260 | -0.677211 | -2.158420 | 0.5908 | 0.6097 | 0.3790 | nan | nan |
| 2459980 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.135947 | 0.588360 | 0.372992 | 1.460203 | -0.826730 | 1.680711 | -0.741532 | 0.207087 | 0.6358 | 0.6466 | 0.3189 | nan | nan |
| 2459979 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.081518 | 0.724425 | 0.363546 | 1.514790 | -0.889184 | 1.518431 | -1.134690 | -2.151575 | 0.5875 | 0.6107 | 0.3812 | nan | nan |
| 2459978 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.009708 | 0.774318 | 0.505880 | 1.812502 | -0.966202 | 2.166310 | -1.401370 | -2.527830 | 0.5839 | 0.6054 | 0.3883 | nan | nan |
| 2459977 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.028555 | 0.992398 | 0.369998 | 1.433789 | -0.148479 | 1.852183 | -1.202901 | -2.162140 | 0.5548 | 0.5718 | 0.3456 | nan | nan |
| 2459976 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.068475 | 0.818236 | 0.352584 | 1.733252 | -0.485328 | 2.348420 | -0.476889 | -1.878955 | 0.5980 | 0.6170 | 0.3840 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.223608 | -0.305786 | 1.168818 | -0.325643 | 1.610320 | -1.016927 | 2.223608 | -0.601211 | -1.909023 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 1.907531 | 1.291554 | 0.091841 | 1.907531 | -0.339463 | 1.869974 | -0.721738 | 0.474583 | -0.668772 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 1.414839 | 0.012414 | 0.911010 | -0.545265 | 1.400825 | 1.348095 | 1.414839 | -0.625062 | -2.060348 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.396548 | -0.133336 | 0.815620 | -0.350076 | 1.384748 | -1.040652 | 2.396548 | -0.672886 | -1.943562 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.071446 | -0.081753 | 0.755540 | -0.106474 | 1.507832 | -1.024270 | 2.071446 | -0.392997 | -3.108375 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 1.945032 | -0.073028 | 1.118047 | -0.337671 | 1.644562 | -1.178603 | 1.945032 | -0.750841 | -1.447168 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.277508 | -0.009896 | 0.894489 | -0.324555 | 1.666186 | -1.046480 | 2.277508 | -0.813605 | -1.789803 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 1.926993 | 0.124332 | 0.792943 | -0.273037 | 1.516176 | -1.108793 | 1.926993 | -0.750606 | -1.981585 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.293846 | 0.516058 | 1.443162 | -0.056784 | 1.760495 | -0.854307 | 2.293846 | -0.429399 | -1.734360 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.236178 | 0.575933 | 1.135149 | -0.189094 | 1.795240 | -0.751342 | 2.236178 | -0.301760 | -1.827805 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.445525 | 1.110711 | 0.420048 | 1.915465 | -0.219910 | 2.445525 | -0.746005 | -1.713274 | 0.213195 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 1.670375 | 1.023737 | 0.198505 | 1.670375 | 0.006582 | 1.578307 | -1.001253 | -2.016665 | -0.479915 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.890034 | 1.434347 | 0.402914 | 1.964339 | -0.200704 | 2.890034 | -0.983071 | -1.737267 | -0.603398 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 1.507241 | -0.134490 | 0.814139 | -0.230716 | 1.507241 | -0.801897 | 1.506969 | 0.505154 | -0.669629 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.579377 | 1.382886 | 0.402236 | 1.871140 | -0.231229 | 2.579377 | -0.975592 | 0.764554 | -0.773249 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Shape | 1.578542 | 1.578542 | 0.057692 | 1.471238 | -0.350049 | 1.280298 | 1.070859 | -2.752312 | -0.497144 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | ee Shape | 12.980854 | 12.980854 | 7.045926 | 4.346316 | 1.340158 | 9.593825 | 2.800261 | 4.548056 | 1.711592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.593406 | 0.002944 | 0.854672 | 0.545468 | 1.803028 | -0.304304 | 2.593406 | -0.294674 | -0.476995 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Power | 1.036449 | -0.131301 | 0.111655 | -0.088910 | 1.036449 | -0.750819 | -0.158411 | -0.864113 | -0.349317 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.717260 | 0.857183 | 0.033850 | 2.205754 | 0.640668 | 2.717260 | -0.309054 | -2.158420 | -0.677211 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 1.680711 | 0.588360 | -0.135947 | 1.460203 | 0.372992 | 1.680711 | -0.826730 | 0.207087 | -0.741532 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 1.518431 | 0.081518 | 0.724425 | 0.363546 | 1.514790 | -0.889184 | 1.518431 | -1.134690 | -2.151575 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.166310 | 0.774318 | 0.009708 | 1.812502 | 0.505880 | 2.166310 | -0.966202 | -2.527830 | -1.401370 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 1.852183 | -0.028555 | 0.992398 | 0.369998 | 1.433789 | -0.148479 | 1.852183 | -1.202901 | -2.162140 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48 | N06 | not_connected | nn Temporal Variability | 2.348420 | 0.818236 | 0.068475 | 1.733252 | 0.352584 | 2.348420 | -0.485328 | -1.878955 | -0.476889 |